Macroeconomic forecasting in times of crises
提出一种基于聚类和相似性的半参数方法，将序列分块后匹配最近似块进行预测，在1999-2015年特别是大衰退期间优于多种参数模型，对劳动力市场等关键变量尤其有效。
Summary We propose a parsimonious semiparametric method for macroeconomic forecasting. Based on ideas of clustering and similarity, we partition the series into blocks, search for the closest blocks to the latest block of observations, and forecast with the matched blocks. In a real‐time forecasting exercise, we show that our approach does especially well for labor market and other key macro variables. Our method outperforms parametric linear, nonlinear, time‐varying, and combination forecasts for the period 1999–2015 and particularly in the Great Recession. When adding financial spreads, our method delivers further improvements for labor market variables and capacity utilization.